Reason why predicted data become flat

Rule of Decision tree is below.
RandomForest and XGBoost are little bit more compricated.
But base of the architecture is same.

Check input value and choose branch.

End of branch has prediction result.

Threshold values and edge values are fixed by training data.

Important thing is "edge values are fixed by training data".
Edge values are prediction values.
They are fixed in training phase.
After training, even we input bigger data, decision route and result is always maximum ome.

In last example of y=0.5x, maximum value of training data is 3.5 in case of x=7.
So trained model can't predict more than 3.5.